Learning Harris Hawk Algorithm Based on Signal-to-Noise Ratio

Abstract

In view of the problem of insufficient population learning and adaptability of the Harris Hawk optimization algorithm, this paper proposes a learning Harris Hawk algorithm based on signal-to-noise ratio, referred to as SLHHO. This algorithm introduces the concept of signal-to-noise ratio to determine the location information of individuals, and designs a coordinated learning strategy that can more reasonably update the location of individuals within the population, and then redesign the escape distance to improve the adaptation and optimization of the algorithm. ability. Using 12 benchmark functions as standards, the performance of this algorithm was tested with variants of the Harris Eagle algorithm and other algorithms, and comparative analysis was conducted in evaluation indicators such as time complexity, diversity, exploration and development, and the results show that SLHHO has strong competitiveness and feasibility. Finally, the practicality of SLHHO was verified in the pressure vessel design problem.